• What is Data Science
  • How it is different from Big Data and Data Analytics
  • Data Driven decision making
  • Purpose and Business problems
  • How Data Scientist work
  • Skills of a data scientist
  • Different sectors using Data science
  • Real World Applications
  • Future of AI and how the world is changing
  • Introduction to Statistics
    • Statistical and Non-Statistical Analysis
    • Major categories of statistics – Frequency and Bayesian
    • Difference between Statistics and Probabilities
    • Statistical terms
    • Difference between Descriptive Statistics and Inferential Statistics
    • Understanding of Population and Samples
  • Descriptive Statistics
  • Inferential Statistics
  • Central Limit Theorem
  • Types of variables
    • Nominal/Categorical
    • Ordinal
    • Interval/Ratio
    • Continuous, Time Series
  • Central Tendency
  • Mean
  • Median
  • Mode
  • Measure of Statistical dispersions
  • Variance and Bessel correction
  • Standard Deviation
  • Standard Error
  • Margin of Error
  • IQR
  • Range
  • Mean absolute difference
  • median absolute deviation
  • Coefficient of variance
  • Skewness
  • Law of Large Numbers
  • Confidence Level & Interval
  • P value and its interpretation
  • Correlation and auto correlation & correlation matrix
  • Correlation ratio

 

  • Sampling Techniques
  • Sampling errors
  • Sample size estimation
  • Point estimation & margin of error
  • Multi Collinearity
  • Co-variance and correlation
  • P- value and critical value approach
  • T-Distribution and T-Statistics
  • Hypothesis testing’s
  • What is Hypothesis Testing
  • Different types of Errors (Type I and Type II Errors)
  • Z-test
  • T-test
  • Chi-square test
  • ANOVA (one way and two way)
  • F-test & f score
  • P-Value & Significance Level
  • Probability
  • Venn diagram
  • counting (permutation & combination)
  • Expectation
  • Rules of Probabilities
  • Bayesian Network
  • Random Variables and Expected Values
  • Bayes theorem
  • Maximum likelihood estimation
  • Probability Distributions
    • Continuous Distributions- (Normal, uniform, T, F, chi square)
    • “Discrete Distributions- (Bernoulli, binomial, Poisson)
    • Empirical Rules with Z- Score
    •  
  • Why python for data analysis
  • how to install Anaconda
  • Running few simple programs using python
  • “Python objects
    • Lists
    • Strings
    • Tuples
    • Dictionaries”
    • Arrays, Data frames in python
  • “Python Libraries
    • NumPy
    • SciPy
    • Matplotlib
    • Pandas
    • Scikit Learn
    • Seaborn
    • regular expressions
  • Introduction to Series and Data frames
  • Math functions
  • User defined Functions
  • Parameters and arguments of functions
  • Recursive function and its examples
  • “Conditionals in python
    • If loop
    • elif
    • if elif else”
    • “Loops in python
    • for loop
    • while loop”
  • Introduction to pandas
  • Broadcasting in Python
  • Array shape manipulations
  • Data structures in pandas
    • Series
    • Data frame
    • Panel”
  • “Various Data Frame Operations
    • Selection
    • Deletion etc.
    • “Grouping, Merging, and Reshaping of Data
  • Creating matrixes using NumPy
  • Statistical operators using NumPy
  • Basics of data categorization and different formats of data
    • Structured Data
    • Unstructured Data
    • Time Series
  • Why and how to raise the right question
  • Correlation is not the causation and its importance
  • Limitations as a data scientist
  • Transformation of intuition-based decision making to data driven
  • Story Telling
  • Data Analytics Process
  • Exploratory Data Analysis(EDA)
  • How to start with Data Analytics Project
  • Intro to Web Scrapping and Beautiful Soup
  • Supervised Learning
  • Unsupervised Learning
  • Difference between Classification and Regression
  • Data pre-processing
  • What is data set.
  • What is training set
  • What is test set and need for test set
  • Expectation-Maximization technique for missing value
  • using Gradient
  • Feature scaling
  • binning
  • one hot encoding
  • Feature engineering
  • Outliers treatment
  • Bias and Variance trade off
  • Over fitting and Under fitting
  • Exploratory Data analysis(EDA)
    • Univariate analysis
    • Bivariate Analysis
    • Feature Engineering
    • Variable transformation
    • Variable /Feature Creation
    • Project
  • Supervised Regression Algorithms
    • Simple Linear Regression
    • Multiple Linear Regression
    • Ordinary Least Square(OLS)
    • Decision tree Regression
    • Random Forest Regression
    • GLM (Poisson regression, spline)
    • Support Vector Machines Regression
    • Error and Accuracy
    • Gradient Descent
    • Regularization Techniques
    • Maximum Likelihood estimation(MLE)
    • Probabilistic diagnosis of outliers
    • L2 and L1 Norms
    • Ridge Regression
    • Lasso Regression and ElasticNet
    • Project
  • Supervised Classification Algorithms
    • Logistic regression classification
    • Multiclass Classification using Logistic Regression
    • Decision tree Classification
    • Random Forest classification
    • Support Vector Machines classification
    • What is Naïve  Bayes theorem and the limitation
    • Naïve Bayes Classification
    • Ada boost/ Adaptive – Boosting Algorithm
    • GBM
    • Probability in Classification
    • Creating the log loss formula with entropy
    • Softmax Function
    • MLE in classification
    • Understanding the Neural Networks
    • SVM
    • Gradient Boosting
    • XG Boost (Extreme Gradient Boosting)
    • Project
  • Unsupervised Algorithms
  • K-means Clustering
  • Hierarchical clustering
  • Association Rule Mining
  • KNN Classifier
  • PCA
  • Project
  • Model Evaluation Metrics
  • ROC Curves
  • Confusion matrix
  • Accuracy
  • Recall & Precision
  • Specificity & Sensitivity
  • Receiver Operating Characteristic (ROC) curve
  • Area Under Curve (AUC)
  • F1-Score
  • AIC & BIC Scores
  • R squared & Adjusted R squared
  • RMSE, MSE
  • Model selection Techniques
  • Cross validation
  • Boot strap
  • Model selection using Statistical tests
  • Grid search
  • Evaluation Matrix
  • Natural Language Processing (NLP)
    • What is NLP
    • Cleaning Text
    • Tokenization
    • Term Frequency (TF)
    • Term Frequency – Inverse Document Frequency (TF-IDF)
    • Document Term Matrix
  • AI and Deep Learning
    • Introduction to Deep Learning and Neural Networks
    • Introduction to Linear Algebra
    • Artificial Neural Networks
    • Activation Functions
    • Back Propogation
    • Chain Rule of Differentiation
    • Vanishing Gradient Descent
    • Exploding Gradient Descent
    • Drop Out Layers in Multi Neural Network
    • Deep Learning-Activation Functions-Elu, PRelu,Softmax,Swish And Softplus
    • Weight Initialization Techniques
    • Gradient Descent vs Stochastic Gradient Descent
    • AdaGrad Optimizers
    • Hyper Parameter Tuning
    • CNN
    • CNN vs ANN
    • LSTM
    • Bi-LSTM
  • Generative AI
    • Introduction to Generative AI
    • Introduction to Langchain
    • Memory in Langchain
    • Introduction to Vector Database for AI &Large Language Models (LLM)